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2021.4

Demo Projects

Getting Started

Modules

Toolbox

Random

Overview

The Random Node.

The **Random** **Node** generates a random outcome, usually a number.

This **Node** can be set to three different **Advanced**, **Expert**, and **Standard**). Each of these **Attributes** that are explained below.

`Modes`

(`Modes`

offers a different set of Attributes

Advanced

Generator

This

`Mode`

allows to choose whether the random generator is deterministic or not, and for the deterministic case, the seed to use.Attribute

Type

Description

`Is Deterministic`

Whether the random generator is deterministic or not.

`Seed`

*Is Deterministic*

The

`Seed`

to use for the deterministic random generator.Distribution

This **Mode** has a **Drop-down** menu from which the *probability distribution* used for the random generator can be chosen. Each option offers its own set of **Attributes** with the *probability distribution* parameters.

Attribute

Type

Description

`Distribution`

The *probability distribution* that the random generator will use.

Next, the **Attributes** for each *probability distribution* are described. For each *probability distribution*, the link to its corresponding Wikipedia entry is given.

Attribute

Type

Description

`Probability of 'true'`

The probability that the outcome will be *true*.

Attribute

Type

Description

`Data Type`

Whether the outcome will be an **Int** or **Byte**.

`Probability of 'true'`

The probability that the outcome of each trial is *true*.

`Number of trials`

The number of independent experiments, each one with probability of success

`Probability of 'true'`

.Symmetric *probability distribution*, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.

Attribute

Type

Description

`Mean`

The mean value of the distribution.

`Standard deviation`

The standard deviation of the distribution.

Discrete *probability distribution* that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.

Attribute

Type

Description

`Data Type`

Wheter the outcome will be an **Int** or **Byte**.

`Mean`

The mean value of the distribution.

- Uniform

Attribute

Type

Description

`Data Type`

Whether an **Int**, **Float**, or **Byte** will be generated.

`Minimum`

*Data Type*

The lower bound of the interval from which the random number will be extracted.

`Maximum`

*Data Type*

The upper bound of the interval from which the random number will be extracted.

Expert

Generator

This

`Mode`

allows to choose from a list of several types of random generators.Attribute

Type

Description

`Generator`

The type of random generator to use.

`Seed`

*Generator*

)The

`Seed`

to use for the random generator.Distribution

This **Drop-down** menu from which the *probability distribution* to be used for the random generator can be chosen. Each option offers its own set of **Attributes** with the *probability distribution* parameters.

`Mode`

has a Attribute

Type

Description

`Distribution`

The *probability distribution* that the random generator will use.

Next, the **Attributes** for each *probability distribution* are described. For each *probability distribution*, the link to its corresponding Wikipedia entry is given.

Attribute

Type

Description

`Probability of 'true'`

The probability that the outcome will be *true*.

Attribute

Type

Description

`Data Type`

Whether the outcome will be an **Int** or **Byte**.

`Probability of 'true'`

The probability that the outcome of each trial is *true*.

`Number of trials`

The number of independent experiments performed, each one with probability of success

`Probability of 'true'`

.Attribute

Type

Description

`Location`

Defines where the peak is.

`Scale`

Half the width of the probability density function at half the maximum height.

Attribute

Type

Description

`Degrees of freedom`

Number of independent normal *random variables* that are summed.

Attribute

Type

Description

`Rate`

Rate at which the events in the Poisson process occur.

Limit distribution of properly normalized maxima of a sequence of independent and identically distributed *random variables*.

Attribute

Type

Description

`Location`

Defines where the peak is.

`Scale`

Defines how spread out the values are.

Ratio of two independent *random variables* with chi-squared distributions, each one divided by its corresponding number of degrees of freedom for scaling.

Attribute

Type

Description

`Denominator Dof`

Degrees of freedom of the chi-squared *random variable* in the denominator.

`Numerator DoF`

Degrees of freedom of the chi-squared *random variable* in the numerator.

Maximum entropy probability distribution for a *random variable*, whose mean is the product between the shape and scale, which are the two parameters of the Gamma distribution.

Attribute

Type

Description

`Shape`

Modifies the shape of the probability distribution.

`Scale`

Defines how spread out are the values.

The probability distribution of the number of experiments with a Bernoulli distribution needed to get one success.

Attribute

Type

Description

`Data Type`

Whether the output is an **Int** or **Byte**.

`Probability of 'true'`

The probability of success in the Bernoulli trials.

Probability distribution of a *random variable* whose logarithm has a normal distribution.

Attribute

Type

Description

`Mean`

The mean value of the logarithm of the distribution.

`Standard deviation`

The standard deviation of the logarithm of the distribution.

Attribute

Type

Description

`Data Type`

Whether the outcome is an **Int** or **Byte**.

`Probability of 'true'`

The probability that the outcome of each trial is *true*.

`Number of trials`

The number of failures to occur until the experiments stop.

Symmetric *probability distribution*, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.

Attribute

Type

Description

`Mean`

The mean value of the distribution.

`Standard deviation`

The standard deviation of the distribution.

Discrete *probability distribution* that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.

Attribute

Type

Description

`Data Type`

Wheter the outcome will be an **Int** or **Byte**.

`Mean`

The mean value of the distribution.

Attribute

Type

Description

`Degrees of freedom`

The number of observations taken from a normal distribution minus one. As it grows, the Student-t distribution approaches a normal distribution.

- Uniform

Attribute

Type

Description

`Data Type`

Whether an **Int**, **Float**, or **Byte** will be generated.

`Minimum`

*Data Type*

The lower bound of the interval from which the random number will be extracted.

`Maximum`

*Data Type*

The upper bound of the interval from which the random number will be extracted.

Attribute

Type

Description

`Shape`

Defines the shape of the probability distribution.

`Scale`

Defines how spread out the values of the probability distribution are.

Standard

Distribution

Attribute

Type

Description

`Data Type`

Whether an **Int**, **Float**, or **Byte** will be generated.

`Minimum`

*Data Type*

The lower bound of the interval from which the random number will be extracted.

`Maximum`

*Data Type*

The upper bound of the interval from which the random number will be extracted.

Inputs

Input

Type

Description

A standard **Input Pulse**, to trigger the execution of the **Node**.

Outputs

Output

Type

Description

A standard **Output Pulse**, to move onto the next **Node** along the **Logic Branch**, once this **Node** has finished its execution.

`Output`

*Mode*

*Distribution*

The random outcome that was generated.

External Links

Last modified 1mo ago

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